# Source code for networkx.algorithms.community.kernighan_lin

```
"""Functions for computing the Kernighan–Lin bipartition algorithm."""
from itertools import count
import networkx as nx
from networkx.algorithms.community.community_utils import is_partition
from networkx.utils import BinaryHeap, not_implemented_for, py_random_state
__all__ = ["kernighan_lin_bisection"]
def _kernighan_lin_sweep(edges, side):
"""
This is a modified form of Kernighan-Lin, which moves single nodes at a
time, alternating between sides to keep the bisection balanced. We keep
two min-heaps of swap costs to make optimal-next-move selection fast.
"""
costs0, costs1 = costs = BinaryHeap(), BinaryHeap()
for u, side_u, edges_u in zip(count(), side, edges):
cost_u = sum(w if side[v] else -w for v, w in edges_u)
costs[side_u].insert(u, cost_u if side_u else -cost_u)
def _update_costs(costs_x, x):
for y, w in edges[x]:
costs_y = costs[side[y]]
cost_y = costs_y.get(y)
if cost_y is not None:
cost_y += 2 * (-w if costs_x is costs_y else w)
costs_y.insert(y, cost_y, True)
i = 0
totcost = 0
while costs0 and costs1:
u, cost_u = costs0.pop()
_update_costs(costs0, u)
v, cost_v = costs1.pop()
_update_costs(costs1, v)
totcost += cost_u + cost_v
i += 1
yield totcost, i, (u, v)
[docs]
@not_implemented_for("directed")
@py_random_state(4)
@nx._dispatchable(edge_attrs="weight")
def kernighan_lin_bisection(G, partition=None, max_iter=10, weight="weight", seed=None):
"""Partition a graph into two blocks using the Kernighan–Lin
algorithm.
This algorithm partitions a network into two sets by iteratively
swapping pairs of nodes to reduce the edge cut between the two sets. The
pairs are chosen according to a modified form of Kernighan-Lin [1]_, which
moves node individually, alternating between sides to keep the bisection
balanced.
Parameters
----------
G : NetworkX graph
Graph must be undirected.
partition : tuple
Pair of iterables containing an initial partition. If not
specified, a random balanced partition is used.
max_iter : int
Maximum number of times to attempt swaps to find an
improvement before giving up.
weight : key
Edge data key to use as weight. If None, the weights are all
set to one.
seed : integer, random_state, or None (default)
Indicator of random number generation state.
See :ref:`Randomness<randomness>`.
Only used if partition is None
Returns
-------
partition : tuple
A pair of sets of nodes representing the bipartition.
Raises
------
NetworkXError
If partition is not a valid partition of the nodes of the graph.
References
----------
.. [1] Kernighan, B. W.; Lin, Shen (1970).
"An efficient heuristic procedure for partitioning graphs."
*Bell Systems Technical Journal* 49: 291--307.
Oxford University Press 2011.
"""
n = len(G)
labels = list(G)
seed.shuffle(labels)
index = {v: i for i, v in enumerate(labels)}
if partition is None:
side = [0] * (n // 2) + [1] * ((n + 1) // 2)
else:
try:
A, B = partition
except (TypeError, ValueError) as err:
raise nx.NetworkXError("partition must be two sets") from err
if not is_partition(G, (A, B)):
raise nx.NetworkXError("partition invalid")
side = [0] * n
for a in A:
side[index[a]] = 1
if G.is_multigraph():
edges = [
[
(index[u], sum(e.get(weight, 1) for e in d.values()))
for u, d in G[v].items()
]
for v in labels
]
else:
edges = [
[(index[u], e.get(weight, 1)) for u, e in G[v].items()] for v in labels
]
for i in range(max_iter):
costs = list(_kernighan_lin_sweep(edges, side))
min_cost, min_i, _ = min(costs)
if min_cost >= 0:
break
for _, _, (u, v) in costs[:min_i]:
side[u] = 1
side[v] = 0
A = {u for u, s in zip(labels, side) if s == 0}
B = {u for u, s in zip(labels, side) if s == 1}
return A, B
```